Abstract: The present disclosure describes techniques for entity classification and data enrichment of data sets. A data enrichment system is disclosed that can extract, repair, and enrich datasets, resulting in more precise entity resolution and classification for purposes of subsequent indexing and clustering. Disclosed techniques may include performing entity recognition to identify segments of interest that relate to an entity. Related data may be analyzed for classification, which can be used to transform the data for enrichment to its users.

Abstract: The present disclosure relates generally to a data enrichment service that automatically profiles data sets and provides visualizations of the profiles using a visual-interactive model within a client application (such as a web browser or mobile app). The visual profiling can be refined through end user interaction with the visualization objects and guide exploratory data visualization and discovery. Additionally, data sampling of heterogeneous data streams can be performed during ingestion to extract statistical attributes from multi-columnar data (e.g., standard deviation, median, mode, correlation coefficient, histogram, etc.). Data sampling can continue in real-time as data sources are updated.

Abstract: A search system associates contextual metadata with search terms and/or stored terms to facilitate identification of relevant information. In one implementation, a search term is identified (4304) from a received search request. The search term is then rewritten (4306) in standard form and the standard form term is then set (4308) as the current search parameter. A source database is then searched (4310) using the current search parameter. If any results are obtained (4312) these results may be output (4320) to the user. If no results are obtained, a parent classification of the search term is set (4316) as the current search parameter and the process is repeated. The invention thereby provides the ease of use of term searching with the comprehensiveness of category searching.

Abstract: The present disclosure relates generally to a data enrichment service that automatically profiles data sets and provides visualizations of the profiles using a visual-interactive model within a client application (such as a web browser or mobile app). The visual profiling can be refined through end user interaction with the visualization objects and guide exploratory data visualization and discovery. Additionally, data sampling of heterogeneous data streams can be performed during ingestion to extract statistical attributes from multi-columnar data (e.g., standard deviation, median, mode, correlation coefficient, histogram, etc.). Data sampling can continue in real-time as data sources are updated.

Abstract: The present disclosure describes techniques for entity classification and data enrichment of data sets. A data enrichment system is disclosed that can extract, repair, and enrich datasets, resulting in more precise entity resolution and classification for purposes of subsequent indexing and clustering. Disclosed techniques may include performing entity recognition to identify segments of interest that relate to an entity. Related data may be analyzed for classification, which can be used to transform the data for enrichment to its users.

Abstract: A machine based tool and associated logic and methodology used in converting data from an input form to a target form using context dependent conversion rules. A frame-slot architecture is utilized where a frame represents an intersection between a contextual cue recognized by the machine tool, associated content and related constraint information to specific to that conversion environment. A slot represents an included chunk of information. An exemplary conversion system (400) includes a parser (402) for use in parsing and converting an input stream (403) to provide an output stream (411) in a form for use by a target system (412). The parser (402) uses information from a public schema (406), a private schema (408) and a grammar (410). The public schema (406), private schema (408) and grammar (410) may include conversion rules applicable to less than the whole of a subject matter area including the input stream (403).

Abstract: A semantic conversion system (1900) includes a self-learning tool (1902). The self-learning tool (1902) receives input files from legacy data systems (1904). The self-learning tool (1902) includes a conversion processor (1914) that can calculate probabilities associated with candidate conversion terms so as to select an appropriate conversion term. The self-learning tool (1902) provides a fully attributed and normalized data set (1908).

Abstract: A semantic conversion system (1900) includes a self-learning tool (1902). The self-learning tool (1902) receives input files from legacy data systems (1904). The self-learning tool (1902) includes a conversion processor (1914) that can calculate probabilities associated with candidate conversion terms so as to select an appropriate conversion term. The self-learning tool (1902) provides a fully attributed and normalized data set (1908).

Abstract: A search system associates contextual metadata with search terms and/or stored terms to facilitate identification of relevant information. In one implementation, a search term is identified (4304) from a received search request. The search term is then rewritten (4306) in standard form and the standard form term is then set (4308) as the current search parameter. A source database is then searched (4310) using the current search parameter. If any results are obtained (4312) these results may be output (4320) to the user. If no results are obtained, a parent classification of the search term is set (4316) as the current search parameter and the process is repeated. The invention thereby provides the ease of use of term searching with the comprehensiveness of category searching.

Abstract: A search system associates contextual metadata with search terms and/or stored terms to facilitate identification of relevant information. In one implementation, a search term is identified (4304) from a received search request. The search term is then rewritten (4306) in standard form and the standard form term is then set (4308) as the current search parameter. A source database is then searched (4310) using the current search parameter. If any results are obtained (4312) these results may be output (4320) to the user. If no results are obtained, a parent classification of the search term is set (4316) as the current search parameter and the process is repeated. The invention thereby provides the ease of use of term searching with the comprehensiveness of category searching.

Abstract: A search system associates contextual metadata with search terms and/or stored terms to facilitate identification of relevant information. In one implementation, a search term is identified (4304) from a received search request. The search term is then rewritten (4306) in standard form and the standard form term is then set (4308) as the current search parameter. A source database is then searched (4310) using the current search parameter. If any results are obtained (4312) these results may be output (4320) to the user. If no results are obtained, a parent classification of the search term is set (4316) as the current search parameter and the process is repeated. The invention thereby provides the ease of use of term searching with the comprehensiveness of category searching.

Abstract: A machine based tool and associated logic and methodology arc used in converting data from an input form to a target form using context dependent conversion rules. A frame-slot architecture is utilized where a frame represents an intersection between a contextual cue recognized by the machine tool, associated content and related constraint information to specific to that conversion environment. A slot represents an included chunk of information. An exemplary conversion system (400) includes a parser (402) for use in parsing and converting an input stream (403) to provide an output stream (411) in a form for use by a target system (412). The parser (402) uses information from a public schema (406), a private schema (408) and a grammar (410). The public schema (406), private schema (408) and grammar (410) may include conversion rules applicable to less than the whole of a subject matter area including the input stream (403).

Abstract: A machine based tool and associated logic and methodology are used in converting data from an input form to a target form using context dependent conversion rules. In particular, a frame-slot architecture is utilized where a frame represents an intersection between a contextual cue recognized by the machine tool, associated content and related constraint information to specific to that conversion environment, whereas a slot represents an included chunk of information. An exemplary conversion system (400) includes a parser (402) for use in parsing and converting an input stream (403) from a source (404) to provide an output stream (411) in a form for use by a target system (412). To accomplish the desired conversion, the parser (402) uses information from a public schema (406), a private schema (408) and a grammar (410). The public schema (406), private schema (408) and grammar (410) may include conversion rules applicable to less than the whole of a subject matter area including the input stream (403).

Abstract: In connection with a machine-based system for transforming data from a source form to a target form, a tool is provided for sharing information established in developing a transformation model. The shared information may relate to rules for mapping source collection terms to standardized terms, rules for ordering or syntax, rules for classifying terms or other transformation rules. Such information defines a knowledge base for the project. The knowledge base is divided into domains and subdomains that define a hierarchy that includes dependencies and references. These dependencies and references are used for domain management such that any conflicts and inconsistencies can be identified and arbitrated. In this manner, information sharing is accommodated, for example, for efficient knowledge base creation and multi-user functionality.

Abstract: A machine based tool and associated logic and methodology are used in converting data from an input form to a target form using context dependent conversion rules. In particular, a frame-slot architecture is utilized where a frame represents an intersection between a contextual cue recognized by the machine tool, associated content and related constraint information to specific to that conversion environment, whereas a slot represents an included chunk of information. An exemplary conversion system (400) includes a parser (402) for use in parsing and converting an input stream (403) from a source (404) to provide an output stream (411) in a form for use by a target system (412). To accomplish the desired conversion, the parser (402) uses information from a public schema (406), a private schema (408) and a grammar (410). The public schema (406), private schema (408) and grammar (410) may include conversion rules applicable to less than the whole of a subject matter area including the input stream (403).

Abstract: A method and apparatus are disclosed for transforming information from one semantic environment to another. In one implementation, a SOLx system (1700) includes a Normalization/Translation NorTran Workbench (1702) and a SOLx server (1708). The NorTran Workbench (1702) is used to develop a knowledge base based on information from a source system (1712), to normalize legacy content (1710) according to various rules, and to develop a database (1706) of translated content. During run time, the SOLx server (1708) receives transmissions from the source system (1712), normalizes the transmitted content, accesses the database (1706) of translated content and otherwise translates the normalized content, and reconstructs the transmission to provide substantially real-time transformation of electronic messages.

Abstract: A semantic conversion system (1900) includes a self-learning tool (1902). The self-learning tool (1902) receives input files from legacy data systems (1904). The self-learning tool (1902) includes a conversion processor (1914) that can calculate probabilities associated with candidate conversion terms so as to select an appropriate conversion term. The self-learning tool (1902) provides a fully attributed and normalized data set (1908).

Abstract: A semantic conversion system (1900) includes a self-learning tool (1902). The self-learning tool (1902) receives input files from legacy data systems (1904). The self-learning tool (1902) includes a conversion processor (1914) that can calculate probabilities associated with candidate conversion terms so as to select an appropriate conversion term. The self-learning tool (1902) provides a fully attributed and normalized data set (1908).

Abstract: A machine based tool and associated logic and methodology are used in converting data from an input form to a target form using context dependent conversion rules. In particular, a frame-slot architecture is utilized where a frame represents an intersection between a contextual cue recognized by the machine tool, associated content and related constraint information to specific to that conversion environment, whereas a slot represents an included chunk of information. An exemplary conversion system (400) includes a parser (402) for use in parsing and converting an input stream (403) from a source (404) to provide an output stream (411) in a form for use by a target system (412). To accomplish the desired conversion, the parser (402) uses information from a public schema (406), a private schema (408) and a grammar (410). The public schema (406), private schema (408) and grammar (410) may include conversion rules applicable to less than the whole of a subject matter area including the input stream (403).

Abstract: A method and apparatus are disclosed for transforming information from one semantic environment to another, for example, to facilitate electronic information searches. In one implementation, a SOLx system 1700 includes a Normalization/Translation NorTran Workbench 1702 and a SOLx server 1708. The NorTran Workbench 1702 is used to develop a knowledge base based on information from a source system 1712, to normalize legacy content 1710 according to various rules, and to develop a database 1706 of translated content. During run time, the SOLx server 1708 receives transmissions from the source system 1712, normalizes the transmitted content, accesses the database 1706 of translated content and otherwise translates the normalized content, and reconstructs the transmission to provide substantially real-time transformation of electronic messages. Additionally, content can be classified relative to a taxonomy defining relationships between terms or items so as to facilitate electronic searching or other processing.